一种自适应的非参数判别分析方法

An Adaptive Nonparametric Discriminant Analysis Method

线性判别分析(LDA)是在包括人脸识别等多个应用领域被广泛采用的降维方法。但是,由于LDA是基于各类均服从高斯分布的假设,导致其类间散度矩阵的定义会产生相邻类别的重叠问题。因此,我们提出了一种自适应的非参数判别分析方法(ANDA),此方法通过增加位于类边界附近样本点在类间散度矩阵中的权重的方法来增大不同类的相邻样本点之间的距离。本文通过在FERET以及ORL人脸库上的实验把ANDA方法与传统的PCA+LDA,Orthogonal LDA(OLDA)和非参数判别分析(NDA)进行了比较,实验结果表明本文提出的方法优于其他方法。

Linear Discriminant Analysis （LDA） is frequently used for dimension reduction and has been successfully utilized in many applications,especially face recognition. In classical LDA, however, the definition of the between-class scatter matrix can cause large overlaps between neighboring classes, because LDA assumes that all classes obey a Gaussian distribution with the same eovarianee. We therefore, propose an adaptive nonparametrie discriminant analysis （ANDA） algorithm that maximizes the distance between neighboring samples belonging to different classes, thus improving the discriminating power of the samples near the classification borders. To evaluate its performance thoroughly, we have compared our ANDA algorithm with traditional PCA＋LDA, Orthogonal LDA （OLDA） and nonparametric discriminant analysis （NDA） on the FERET and ORL face databases. Experimental results show that the proposed algorithm outperforms the others.